--- pretty_name: pi-embodied Robotics R&D Multimodal Interactions language: - en - zh configs: - config_name: default data_files: - split: train path: data/train-*.parquet dataset_info: features: - name: id dtype: string - name: schema_version dtype: string - name: trajectory_unit dtype: string - name: source dtype: string - name: session_id dtype: string - name: turn_id dtype: string - name: model dtype: string - name: messages list: - name: role dtype: string - name: content list: - name: type dtype: string - name: text dtype: string - name: path dtype: string - name: mime_type dtype: string - name: tool_calls list: - name: id dtype: string - name: type dtype: string - name: name dtype: string - name: arguments dtype: string - name: tool_call_id dtype: string - name: name dtype: string - name: timestamp dtype: string - name: messages_json dtype: string - name: tools_json dtype: string - name: tools_status dtype: string - name: images list: image - name: media_refs sequence: string - name: media_status dtype: string - name: outcome dtype: string - name: repository_url dtype: string - name: base_commit dtype: string - name: patch dtype: string - name: test_result_json dtype: string - name: provenance_json dtype: string - name: native_records_json dtype: string - name: lerobot_status dtype: string --- # pi-embodied Robotics R&D Multimodal Interactions This public dataset contains 2,396 project-scoped agent turns, including 184 turns with recoverable, published images. It preserves successful, unsuccessful, interrupted and unverified interactions without inventing outcome labels. ## Storage - `data/train-*.parquet`: normalized interactions with an explicit Arrow schema and Zstandard compression. - `media//.`: deduplicated original image bytes, referenced by messages and exposed through the Hugging Face `images` feature. - `manifest.json`: row counts, media recovery status and image checksums. - `data/batches/` and `manifests/`: immutable historical text archives, excluded from the default dataset configuration. - `load_multimodal.py`: loading helpers for interactions, chat messages and execution exports. ## Conversation schema Each row corresponds to a previously selected task turn, not a certified full robot episode. `messages` stores ordered user/assistant/tool messages, content parts, call IDs, function names, arguments and timestamps. `messages_json` stores an OpenAI-style conversation with `tool_calls.function` and `image_url` content parts. Decode it with `json.loads`; image URLs resolve to this repository. Visible reasoning summaries are retained when available; encrypted provider content is excluded. `images` is a list of Hugging Face Image features; `media_refs` identifies the corresponding repository-relative files. Original source records, after media extraction and text redaction, are available in `native_records_json`. `provenance_json` links every normalized row to its immutable archive identity. The same row IDs and source classification are retained. `tools_json` is `[]` and `tools_status` is `not_recorded` where the original collection did not preserve tool definitions. Tool definitions are not reconstructed from argument examples. Repository revisions, patches, test results and task outcomes remain null or unknown when not independently linked to the recorded turn. ## Media recovery and handling Original inline images were recovered from source logs for previously selected turns. Recoverable explicit local image attachments are included. Referenced files that no longer exist remain marked as missing. Remote URLs are not crawled to reconstruct unavailable content. Text is filtered for recognizable credentials and personal identifiers. Visual outputs of known robot execution and observation tools are retained with their source provenance; other images undergo OCR filtering for recognizable sensitive content. OCR is a filter rather than a guarantee. Original source logs and withheld images remain on their private source/staging storage. The default `train` split denotes a usable storage split, not a claim that all examples are suitable for supervised training. Outcome-based curation, overlapping-history deduplication, context completion and tool-schema capture remain necessary for individual training recipes. No new redistribution or training license is assigned; original third-party material retains its applicable rights.